摘要
提出基于萤火虫群优化的小波常模盲均衡算法(GSO-WT-CMA),利用萤火虫群优化(GSO)算法良好的全局搜索能力来优化均衡器的初始权向量,结合正交小波变换(WT)降低信号自相关性,有效改善了常数模盲均衡算法(CMA)容易陷入局部最小值、收敛速度慢、稳态误差大等问题.水声仿真结果表明,新算法具有收敛速度快,均方误差小,能获得全局最优解等优点.
An orthogonal wavelet transform (WT) constant modulus blind equalization algorithm(CMA) based on artifieial glowworm swarm optimization algorithm(GSO)is proposed. In the proposed algo- rithm,CMA is integrated with GSO and WT, the de-correlation ability of WT is used to reduce the signal autocorrelation,and the global search ability of GSO algorithm is used to optimize the equalizer weight vector. It can reduce the mean square error of CMA and the possibility of CMA local convergence and im- prove the convergence rate of CMA. The results from computer simulation show that the proposed algo- rithm outperforms the CMA and WT-CMA in improving the convergence rate and reducing the steady- state error and computational loads.
出处
《安徽工程大学学报》
CAS
2013年第1期91-94,共4页
Journal of Anhui Polytechnic University
基金
全国优秀博士学位论文作者专项基金资助项目(200753)
安徽省高等学校自然科学基金资助项目(KJ2010A096)
安徽高校省级科研基金资助项目(KJ2011B162)
江苏省"六大人才高峰"培养基金资助项目(2008026)
淮南职业技术学院院级科研基金资助项目(HKJ10-3)
关键词
盲均衡
水声信道
常模
小波变换
萤火虫群
智能优化
blind equalization
underwater acoustic communieation
constant modulus
wavelet trans- form
artificial glowworm swarm
intelligent optimization algorithm